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Students’ emotional state and attention significantly impact how they handle stress and interact with their studies. These factors are crucial in defining their learning objectives and general personal growth, influencing their academic achievement. Because Bangladesh has distinct educational and mental health difficulties, it is important to comprehend these dynamics. Time series analysis is a useful technique for tracking lecture activities in class and how they affect student participation since it provides insightful information about behavior patterns across time. This investigation aims to address these problems by using MotionWatch 8 and a comprehensive questionnaire to analyze class lecture activities. This study employs ensemble methodologies, deep Learning algorithms, and a diverse range of machine learning models to assess and predict student behavior. A hybrid model is one of the techniques that produced the most stunning results, proving how well it could capture complex patterns in time series data. To assess the robustness of the algorithms, the study also looked at how different datasets performed. Ultimately, the models’ interpretability was improved, and their decision-making processes were given a more profound understanding by utilizing explainable AI methods, including SHAP, LIME, and permutation importance. The effort establishes new standards for improving student engagement and well-being through data-driven insights, sophisticated models, and explainable AI. |
